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Observability

Grafana Loki

An open source logging system that is horizontally-scalable, multi-tenant log aggregation system inspired by Prometheus, designed to be cost-effective and easy to operate

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Summary

An open-source logging system that offers efficient log aggregation, indexing, and querying for cloud-native applications. It leverages the same distributed design principles as Prometheus, allowing it to scale horizontally while maintaining low resource usage. Loki provides powerful query capabilities, enabling users to filter, aggregate, and visualize log data easily. With its efficient storage backend and support for various logging formats, Loki simplifies log management and analysis for modern, containerized environments.

Key Features

1. Built with distributed design principles, Loki scales horizontally to handle large volumes of log data efficiently.
2. Loki utilizes a highly efficient storage backend, allowing it to store vast amounts of log data without consuming excessive resources.
3. Loki provides powerful query capabilities, enabling users to filter, aggregate, and analyze log data using PromQL, a Prometheus-inspired query language.
4. Loki seamlessly integrates with Grafana, allowing users to visualize log data alongside other metrics and data sources for comprehensive monitoring and observability.
5. Designed for cloud-native environments, Loki natively supports Kubernetes and other container orchestration platforms, making it easy to deploy and manage in modern application environments.

How does it work?

Real world example

Pros

1. With its efficient storage backend, Loki can store vast amounts of log data without consuming excessive resources, making it a cost-effective solution for log management.
2. Loki seamlessly integrates with Grafana, providing users with powerful visualization capabilities to analyze log data alongside other metrics and data sources.
3. Loki is well-suited for cloud-native environments and natively supports Kubernetes and other container orchestration platforms, simplifying deployment and management.
4. Loki leverages PromQL, a powerful query language inspired by Prometheus, allowing users to perform complex queries and analysis on log data.

Cons

1. Setting up and configuring Loki may require some technical expertise, especially for users unfamiliar with distributed systems and cloud-native technologies.
2. While Loki is designed to be efficient with resources, scaling to handle extremely large volumes of log data may require significant hardware resources and infrastructure.
3. Loki primarily relies on its own storage backend for storing log data, which may not offer the same flexibility or features as other log management solutions with more extensive storage options.

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Capillary reduced ops tickets by 95%

“Our releases are fast. And with less developer time needed our teams can focus on building exciting features. We’ve saved countless hours and costs.”

Piyush K,
Chief Architect, Capillary Technologies,

Treebo reduced production issues by 70%

"With Facets, our staging environments look identical to production environments. So in case of production issues, we can be sure there are no infra drifts."

Kadam Jeet Jain,
Co-Founder & CTO, Treebo Hotels and Hotel Superhero.

GGX switched from AWS to GCP in 2 weeks

"Facets has radically changed our DevOps for the better. They did all the heavy lifting and saved us precious time and resources in our when we switched from AWS to GCP."

Kaustubh Bhoyar,
Head of engineering, GGX

Trusted by companies to run production at scale

Capillary | FacetsMPL | FacetsTreebo | FacetsPurplle | Facets

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